Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic evaluation of counterfactual queries
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
The independent choice logic for modelling multiple agents under uncertainty
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On the logic of causal explanation
Artificial Intelligence
Artificial Intelligence - Special issue on relevance
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
A logic of universal causation
Artificial Intelligence
Causality: models, reasoning, and inference
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Knowlege in action: logical foundations for specifying and implementing dynamical systems
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Default Reasoning: Causal and Conditional Theories
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Complexity results for structure-based causality
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Reasoning with Cause and Effect
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Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
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Reasoning about actions in a probabilistic setting
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Strategies for determining causes of events
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Symbolic dynamic programming for first-order MDPs
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Causal theories for nonmonotonic reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Explanation, irrelevance and statistical independence
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Causal theories of action and change
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Causes and explanations in the structural-model approach
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Defining explanation in probabilistic systems
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Causes and explanations in the structural-model approach: tractable cases
Artificial Intelligence
Probabilistic description logic programs
International Journal of Approximate Reasoning
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Encoding probabilistic causal model in probabilistic action language
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Cp-logic: A language of causal probabilistic events and its relation to logic programming
Theory and Practice of Logic Programming
Causes and explanations in the structural-model approach: Tractable cases
Artificial Intelligence
Tightly integrated probabilistic description logic programs for representing ontology mappings
FoIKS'08 Proceedings of the 5th international conference on Foundations of information and knowledge systems
Embracing events in causal modelling: interventions and counterfactuals in CP-ogic
JELIA'10 Proceedings of the 12th European conference on Logics in artificial intelligence
Probabilistic description logic programs
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Tightly integrated probabilistic description logic programs for representing ontology mappings
Annals of Mathematics and Artificial Intelligence
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This paper is directed towards combining Pearl's structural-model approach to causal reasoning with high-level formalisms for reasoning about actions. More precisely, we present a combination of Pearl's structural-model approach with Poole's independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structural-model approach. We illustrate this along Halpern and Pearl's sophisticated notions of actual cause, explanation, and partial explanation. This mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach.